| Title |
A Study on the Impact of International Exchange Complex Development on the Housing Market : Focusing on the Effect of Policy Environment on Auction Winning Bid Ratios |
| Authors |
Min-Jeong Joo ; Sangyoub Lee |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2026.27.2.032 |
| Keywords |
Apartment Auction; Winning Bid Rate; International Exchange Complex (IEC); Policy Regulation; Spatial Econometric Model; Machine Learning |
| Abstract |
This study analyzes the multi-layered impacts of policy regulations?specifically financial restrictions and land transaction permit zones?and the development expectations of the International Exchange Complex (IEC) on apartment auction winning bid rates in Gangnam-gu. By integrating OLS, Machine Learning (ML), and Spatial Econometric (SAR) models, this research aimed to enhance both analytical robustness and predictive accuracy. The ML models (Random Forest, LightGBM) demonstrated superior predictive power (R²?0.507), validating the significance of complex, non-linear relationships within the market. Crucially, the SAR model effectively controlled for spatial autocorrelation found in OLS residuals. The non-significant spatial coefficient (ρ) supported the statistical stability of the OLS estimates, suggesting that spatial spillover effects are minimal in the auction market. Consequently, ... policy variables exhibited significant positive direct effects, implying that regulatory restrictions in the general market induced a balloon effect in the auction market. The results of this study first confirmed the potential for "regulation," a risk factor for development projects, to be transformed into "opportunities" depending on market conditions. Furthermore, it proposed criteria for defining the effective sphere of influence for large-scale urban development projects. Finally, this study, through an analytical approach combining machine learning and spatial econometric models, suggests the potential for enhancing a PropTech-based valuation model, offering important practical implications for the construction and real estate development sectors. |